Stochastic Model Predictive Control for Autonomous Mobility on Demand

This paper presents a stochastic, model predictive control (MPC) algorithm that leverages short-term probabilistic forecasts for dispatching and rebalancing Autonomous Mobility-on-Demand systems (AMoD, i.e. fleets of self-driving vehicles). We first present the core stochastic optimization problem in terms of a time-expanded network flow model. Then, to ameliorate its tractability, we present two key relaxations. First, we replace the original stochastic problem with a Sample Average Approximation (SAA), and characterize the performance guarantees. Second, we separate the controller into two separate parts to address the task of assigning vehicles to the outstanding customers separate from that of rebalancing. This enables the problem to be solved as two totally unimodular linear programs, and thus easily scalable to large problem sizes. Finally, we test the proposed algorithm in two scenarios based on real data and show that it outperforms prior state-of-the-art algorithms. In particular, in a simulation using customer data from DiDi Chuxing, the algorithm presented here exhibits a 62.3 percent reduction in customer waiting time compared to state of the art non-stochastic algorithms.Comment: Submitting to the IEEE International Conference on Intelligent Transportation Systems 201

AIRO 2016. 46th Annual Conference of the Italian Operational Research Society. Emerging Advances in Logistics Systems Trieste, September 6-9, 2016 - Abstracts Book

The AIRO 2016 book of abstract collects the contributions from the conference participants. The AIRO 2016 Conference is a special occasion for the Italian Operations Research community, as AIRO annual conferences turn 46th edition in 2016. To reflect this special occasion, the Programme and Organizing Committee, chaired by Walter Ukovich, prepared a high quality Scientific Programme including the first initiative of AIRO Young, the new AIRO poster section that aims to promote the work of students, PhD students, and Postdocs with an interest in Operations Research. The Scientific Programme of the Conference offers a broad spectrum of contributions covering the variety of OR topics and research areas with an emphasis on “Emerging Advances in Logistics Systems”. The event aims at stimulating integration of existing methods and systems, fostering communication amongst different research groups, and laying the foundations for OR integrated research projects in the next decade. Distinct thematic sections follow the AIRO 2016 days starting by initial presentation of the objectives and features of the Conference. In addition three invited internationally known speakers will present Plenary Lectures, by Gianni Di Pillo, Frédéric Semet e Stefan Nickel, gathering AIRO 2016 participants together to offer key presentations on the latest advances and developments in OR’s research

Operational Research IO2017, Valença, Portugal, June 28-30

This proceedings book presents selected contributions from the XVIII Congress of APDIO (the Portuguese Association of Operational Research) held in Valença on June 28–30, 2017. Prepared by leading Portuguese and international researchers in the field of operations research, it covers a wide range of complex real-world applications of operations research methods using recent theoretical techniques, in order to narrow the gap between academic research and practical applications. Of particular interest are the applications of, nonlinear and mixed-integer programming, data envelopment analysis, clustering techniques, hybrid heuristics, supply chain management, and lot sizing and job scheduling problems. In most chapters, the problems, methods and methodologies described are complemented by supporting figures, tables and algorithms. The XVIII Congress of APDIO marked the 18th installment of the regular biannual meetings of APDIO – the Portuguese Association of Operational Research. The meetings bring together researchers, scholars and practitioners, as well as MSc and PhD students, working in the field of operations research to present and discuss their latest works. The main theme of the latest meeting was Operational Research Pro Bono. Given the breadth of topics covered, the book offers a valuable resource for all researchers, students and practitioners interested in the latest trends in this field.info:eu-repo/semantics/publishedVersio

Multi-fidelity modelling approach for airline disruption management using simulation

Disruption to airline schedules is a key issue for the industry. There are various causes for disruption, ranging from weather events through to technical problems grounding aircraft. Delays can quickly propagate through a schedule, leading to high financial and reputational costs. Mitigating the impact of a disruption by adjusting the schedule is a high priority for the airlines. The problem involves rearranging aircraft, crew and passengers, often with large fleets and many uncertain elements. The multiple objectives, cost, delay and minimising schedule alterations, create a trade-off. In addition, the new schedule should be achievable without over-promising. This thesis considers the rescheduling of aircraft, the Aircraft Recovery Problem. The Aircraft Recovery Problem is well studied, though the literature mostly focusses on deterministic approaches, capable of modelling the complexity of the industry but with limited ability to capture the inherent uncertainty. Simulation offers a natural modelling framework, handling both the complexity and variability. However, the combinatorial aircraft allocation constraints are difficult for many simulation optimisation approaches, suggesting that a more tailored approach is required. This thesis proposes a two-stage multi-fidelity modelling approach, combining a low-fidelity Integer Program and a simulation. The deterministic Integer Program allocates aircraft to flights and gives an initial estimate of the delay of each flight. By solving in a multi-objective manner, it can quickly produce a set of promising solutions representing different trade-offs between disruption costs, total delay and the number of schedule alterations. The simulation is used to evaluate the candidate solutions and look for further local improvement. The aircraft allocation is fixed whilst a local search is performed over the flight delays, a continuous valued problem, aiming reduce costs. This is done by developing an adapted version of STRONG, a stochastic trust-region approach. The extension incorporates experimental design principles and projected gradient steps into STRONG to enable it to handle bound constraints. This method is demonstrated and evaluated with computational experiments on a set of disruptions with different fleet sizes and different numbers of disrupted aircraft. The results suggest that this multi-fidelity combination can produce good solutions to the Aircraft Recovery Problem. A more theoretical treatment of the extended trust-region simulation optimisation is also presented. The conditions under which a guarantee of the algorithm's asymptotic performance may be possible and a framework for proving these guarantees is presented. Some of the work towards this is discussed and we highlight where further work is required. This multi-fidelity approach could be used to implement a simulation-based decision support system for real-time disruption handling. The use of simulation for operational decisions raises the issue of how to evaluate a simulation-based tool and its predictions. It is argued that this is not a straightforward question of the real-world result being good or bad, as natural system variability can mask the results. This problem is formalised and a method is proposed for detecting systematic errors that could lead to poor decision making. The method is based on the Probability Integral Transformation using the simulation Empirical Cumulative Distribution Function and goodness of fit hypothesis tests for uniformity. This method is tested by applying it to the airline disruption problem previously discussed. Another simulation acts as a proxy real world, which deviates from the simulation in the runway service times. The results suggest that the method has high power when the deviations have a high impact on the performance measure of interest (more than 20%), but low power when the impact is less than 5%

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios